Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/javimosch/superclinpx agentmods add skills/javimosch/supercli/jar-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/javimosch/supercli/jar-skills)<a href="https://agentmods.dev/skills/javimosch/supercli/jar-skills"><img src="https://agentmods.dev/badge/skills/javimosch/supercli/jar-skills/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/javimosch/supercli/jar-skills"><img src="https://agentmods.dev/badge/skills/javimosch/supercli/jar-skills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.09437 |
| Opus 5.5 | $0.00000 | $0.03775 |
| Sonnet 5.5 | $0.00000 | $0.01887 |
| Haiku 4.5 | $0.00000 | $0.00944 |
Grade C, and why
jar-skills scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -LO https://github.com/javimosch/agentmemory-cli/releases/latest/download/agentmemory-cli-linux-amd64 How it starts
The opening of the file, as written. The whole thing — 992 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Optimization Quad: rtk + context-mode + memory + graphify
Use this skill when analyzing codebases, especially large ones (50+ files, 1000+ lines output). Combines token reduction tools (rtk or context-mode) for quick exploration, agentmemory-cli for cross-session persistence, and graphify's persistent structural analysis for deep understanding.
Four-tool strategy:
- rtk: CLI proxy (60-90% reduction) for direct command usage
- context-mode: MCP server (98% reduction) for programmatic/integrated workflows
- agentmemory-cli: Persistent memory for AI agents — save, search, and recall findings across sessions
- graphify: Knowledge graph builder for persistent structural analysis
When to Use This Skill
Trigger this skill when:
- Analyzing unfamiliar codebases or large projects
- Need both quick exploration AND deep structural understanding
- Building persistent knowledge bases for long-term projects
- Cross-file dependency analysis and impact assessment
- Documentation generation with relationship mapping
- Architecture reviews and refactoring planning
- Recalling previous analysis findings across sessions
- Accumulating project knowledge over time
The Combination Strategy
Choosing Between rtk, SC utility plugins, and context-mode
Three complementary approaches for codebase analysis, each with different strengths:
Choose sc ripgrep / SC utility plugins when:
- Searching codebase for symbols, functions, env vars, config keys
- Getting quick codebase metrics (LOC, complexity, TODO count)
- Mapping project structure (tree view with file metadata)
- Scanning for secrets or security issues
- Pre-analysis before deciding which files to rtk or directories to graphify
- All output is JSON-ready for further processing
Choose rtk when:
- Running commands directly in terminal/CLI
- Need token savings analytics on command output
- Optimizing specific commands (git, ls, test, build)
- Want simple CLI proxy without JSON-RPC complexity
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 992 lines · 0 tokens per session scan C 4b8264051076
jar-skills is a skill published in the GitHub repository javimosch/supercli (56 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 9,437 tokens. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-29.
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temporal
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thisQuarter
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thisWeek
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